I’m very interesting in investigating how can dynamic ecosystem-based management strategies be designed to protect and recover marine resources. In particular, I’m interested in reef-associated predators and their role in ecosystem stability and resilience. What can we learn from studying the populations of reef-associated predators that can inform the design and implementation of dynamic ecosytem-based management ?
I believe that having a streamlined, transparent, and reproducible approach to managing data and conducting scientific analysis is of paramount importance to do interdisciplinary and collaborative work. I’m looking forward to deepening my R skills, to become confortable with GitHub, and to expand my skills in visualization and communication of results.
Currently, I don’t have data related to the specific research question stated above. The data that I’ll use in this assignment pertains to a long term ecological assessment of reef fish populations in the lagoons of Rarotonga and Aitutaki for the years 2002 and 2014. This data has been provided by profesor Hunter Lenihan for his course on Applied Marine Ecology.
# read csv
d = read.csv('data/juanmayorgahenao_hunterdata.csv')
surgeon <- subset(d, Species == "Surgeonfish")
trout <- subset(d, Species == "Coral Trout")
spotted <- subset(d, Species == "Spotted Damselfish")
yellow <- subset(d, Species == "Yellow Damselfish")
densities <- data.frame(surgeon$Adults, trout$Adults, spotted$Adults, yellow$Adults)
colnames(densities) <- c("Surgeon", "Coral Trout", "Spotted Damselfish", "Yellow Damselfish")
# output summary
summary(densities)## Surgeon Coral Trout Spotted Damselfish Yellow Damselfish
## Min. : 20.0 Min. : 4.0 Min. : 19.0 Min. :32.0
## 1st Qu.:192.5 1st Qu.: 31.0 1st Qu.:197.5 1st Qu.:35.0
## Median :305.0 Median : 80.0 Median :388.5 Median :58.0
## Mean :255.0 Mean : 83.5 Mean :426.5 Mean :59.5
## 3rd Qu.:367.5 3rd Qu.:132.5 3rd Qu.:617.5 3rd Qu.:82.5
## Max. :390.0 Max. :170.0 Max. :910.0 Max. :90.0